A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

2021 ◽  
pp. 153537022110492
Author(s):  
Qiangda Chen ◽  
Ning Pu ◽  
Hanlin Yin ◽  
Jicheng Zhang ◽  
Guochao Zhao ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
R Package ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Chemotherapy Response ◽  
Lasso Regression ◽  
Online Data ◽  
Metabolic Signature ◽  
Metabolic Genes

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Development of a Predictive Immune-Related Gene Signature Associated With Hepatocellular Carcinoma Patient Prognosis

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097711
Author(s):  
Jiasheng Lei ◽  
Dengyong Zhang ◽  
Chao Yao ◽  
Sheng Ding ◽  
Zheng Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Related Gene ◽  
Patient Cohort ◽  
Immune Related Gene ◽  
Related Risk

Background: Hepatocellular carcinoma (HCC) remains the third leader cancer-associated cause of death globally, but the etiological basis for this complex disease remains poorly clarified. The present study was thus conceptualized to define a prognostic immune-related gene (IRG) signature capable of predicting immunotherapy responsiveness and overall survival (OS) in patients with HCC. Methods: Five differentially expressed IRG associated with HCC were established the immune-related risk model through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Patients were separated at random into training and testing cohorts, after which the association between the identified IRG signature and OS was evaluated using the “survival” R package. In addition, maftools was leveraged to assess mutational data, with tumor mutation burden (TMB) scores being calculated as follows: (total mutations/total bases) × 106. Immune-related risk term abundance was quantified via “ssGSEA” algorithm using the “gsva” R package. Results: HCC patients were successfully stratified into low-risk and high-risk groups based upon a signature composed of 5 differentially expressed IRGs, with overall survival being significantly different between these 2 groups in training cohort, testing cohort and overall patient cohort ( P = 1.745e-06, P = 1.888e-02, P = 4.281e-07). No association was observed between TMB and this IRG risk score in the overall patient cohort ( P = 0.461). Notably, 19 out of 29 immune-related risk terms differed substantially in the overall patient dataset. These risk terms mainly included checkpoints, human leukocyte antigens, natural killer cells, dendritic cells, and major histocompatibility complex class I. Conclusion: In summary, an immune-related prognostic gene signature was successfully developed and used to predict survival outcomes and immune system status in patients with HCC. This signature has the potential to help guide immunotherapeutic treatment planning for patients affected by this deadly cancer.

scholarly journals A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dakui Luo ◽  
Zezhi Shan ◽  
Qi Liu ◽  
Sanjun Cai ◽  
Qingguo Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Metabolic Signature ◽  
Metabolic Genes ◽  
Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

scholarly journals Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Cuiyun Wu ◽  
Yaosheng Luo ◽  
Yinghui Chen ◽  
Hongling Qu ◽  
Lin Zheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Abstract Background: Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns.Methods: Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package “limma”. Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan–Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the “rms” package.Results: Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes,IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. Patients with lower risk scores generally had a better survival rate than those with higher risk scores. The mortality rate in the high-risk group was 42.02%, while that in the low-risk group was 12.50%. Results of the log-rank test showed significant differences in mortality between the two groups (HR: 4.86; 95% CI: 2.72–8.69; P = 1.01E-08). Subsequently, we assessed the prognostic accuracy of the gene signature using an ROC curve and the results show good sensitivity and specificity, with an average area under the curve (AUC) of 0.81 at 5 years (P < 0.01). Following multivariate adjustment for conventional clinical patterns, the prognostic gene signature remained a powerful and independent factor (HR: 4.70; 95% CI: 2.61–8.38; P = 2.06E-07), confirming its robust predictive ability of overall survival in HCC patients. Finally, a nomogram was established based on the gene signature and four clinicopathological features, which demonstrated an advantageous discriminating ability with the potential to facilitate clinical decision-making in HCC.Conclusion: Our prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

scholarly journals Systematic Analysis of an Invasion-Related 3-Gene Signature and Its Validation as a Prognostic Model for Pancreatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Yuliang Zhang ◽  
Zhehao Liu ◽  
Yonghai Chen ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Molecular Subtype ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Rapid Progression ◽  
Lasso Regression ◽  
Systematic Analysis ◽  
Limma Package ◽  
Subtype A

BackgroundPancreatic adenocarcinoma (PAAD) is a malignant tumor of the digestive system that is associated with a poor prognosis in patients owing to its rapid progression and high invasiveness.MethodsNinety-seven invasive-related genes obtained from the CancerSEA database were clustered to obtain the molecular subtype of pancreatic cancer based on the RNA-sequencing (RNA-seq) data of The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) between subtypes were obtained using the limma package in R, and the multi-gene risk model based on DEGs was constructed by Lasso regression analysis. Independent datasets GSE57495 and GSE62452 were used to validate the prognostic value of the risk model. To further explore the expression of the hub genes, immunohistochemistry was performed on PAAD tissues obtained from a large cohort.ResultsThe TCGA-PAAD samples were divided into two subtypes based on the expression of the invasion-related genes: C1 and C2. Most genes were overexpressed in the C1 subtype. The C1 subtype was mainly enriched in tumor-related signaling pathways, and the prognosis of patients with the C1 subtype was significantly worse than those with the C2 subtype. A 3-gene signature consisting of LY6D, BCAT1, and ITGB6 based on 538 DEGs between both subtypes serves as a stable prognostic marker in patients with pancreatic cancer across multiple cohorts. LY6D, BCAT1, and ITGB6 were over-expressed in 120 PAAD samples compared to normal samples.ConclusionsThe constructed 3-gene signature can be used as a molecular marker to assess the prognostic risk in patients with PAAD.

scholarly journals A novel gene signature for prognosis prediction and chemotherapy response in patients with pancreatic cancer

Aging ◽  
2021 ◽  
Author(s):  
Hongcao Lin ◽  
Chonghui Hu ◽  
Shangyou Zheng ◽  
Xiang Zhang ◽  
Rufu Chen ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Gene Signature ◽  
Chemotherapy Response ◽  
Prognosis Prediction ◽  
Novel Gene

scholarly journals Identification of a Hypoxia-Related Gene Signature and Establishment of a Nomogram for Predicting Prognosis in Esophageal Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Wanyi Xiao ◽  
Peng Tang ◽  
Zhilin Sui ◽  
Xianxian Wu ◽  
Yueyang Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Risk Scores

Abstract BackgroundEsophageal squamous cell carcinoma (ESCC), the major subtype of esophageal cancer in China, has a dismal prognosis. Tumor hypoxia, a typical characteristic of many solid tumors, leads to tumor invasion and poor prognosis. We aimed to develop a hypoxia-based gene signature to assist in the diagnosis and prognosis prediction of ESCC.MethodsWe integrated gene expression files from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to identify potential hypoxia-related genes (HRGs). Euclidean-based consensus clustering was performed on the GSE53625 ESCC samples. Immune cell infiltration, differential expression, and Kaplan-Meier survival analyses were performed. Candidate modules were screened using weighted gene coexpression network analysis and then intersected with differentially expressed genes from clustered subgroups to construct the gene signatures that were verified in internal and external datasets. A nomogram was developed with risk scores and clinicopathological variables. Finally, immunohistochemistry was used to detect the protein expression level of relevant genes. Additionally, the relationships between risk scores and the tumor microenvironment were explored.ResultsA total of 5 potential differentially expressed HRGs and 6 critical HRGs were identified. A prognostic model was constructed by screening key HRGs, and higher risk scores were associated with poorer prognosis. The nomogram based on risk scores predicted the 1-, 3-, and 5-year survival rates well, thus determining prognostic risk stratification. The p53, Wnt, and hypoxia signaling pathways may be some of the regulatory mechanisms of hypoxia associated with the tumor microenvironment. Furthermore, the high expression of BGN and low expression of IL-18 in ESCC tissues was verified and was associated with a shorter survival time.ConclusionsOur study systematically explored HRGs and determined the prognostic value of a 6-hypoxia gene signature. A prognostic model was developed and validated based on this, providing potential prognostic predictors and therapeutic targets for ESCC.

scholarly journals An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ze-jing Liu ◽  
Peng-xiao Hou ◽  
Xi-xing Wang
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Risk Scores ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Prognostic Prediction

Background. A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients. Methods. Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores. Results. Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival. Conclusions. Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.

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